{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d1ae70e1-1354-4aa6-9dc4-73d3c7b016c5",
   "metadata": {},
   "source": [
    "# Direct multi-step forecaster\n",
    "\n",
    "This strategy, commonly known as direct multistep forecasting, is computationally more expensive than the recursive since it requires training several models. However, in some scenarios, it achieves better results. This type of model can be obtained with the `ForecasterDirect` class and can also include one or multiple exogenous variables.\n",
    "\n",
    "\n",
    "Direct multi-step forecasting is a time series forecasting strategy in which a separate model is trained to predict each step in the forecast horizon. This is in contrast to recursive multi-step forecasting, where a single model is used to make predictions for all future time steps by recursively using its own output as input.\n",
    "\n",
    "Direct multi-step forecasting can be more computationally expensive than recursive forecasting since it requires training multiple models. However, it can often achieve better accuracy in certain scenarios, particularly when there are complex patterns and dependencies in the data that are difficult to capture with a single model.\n",
    "\n",
    "This approach can be performed using the `ForecasterDirect` class, which can also incorporate one or multiple exogenous variables to improve the accuracy of the forecasts.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-direct-multi-step-forecasting.png\" style=\"width: 500px\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Diagram of direct multi-step forecasting.</i></font>\n",
    "</p>\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/direct-forecasting-gif.gif\" style=\"width: 500px; padding: 10px; background-color: white; border-radius: 4px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Direct forecasting</i></font>\n",
    "</p>\n",
    "\n",
    "To train a `ForecasterDirect` a different training matrix is created for each model.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-direct-forecasting-matrices.png\" style=\"width: 600px\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Transformation of a time series into matrices to train a direct multi-step forecasting model.</i></font>\n",
    "</p>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "77a4bb4b",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d378f5ff-8092-49bb-80b7-8a3996d19fb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.direct import ForecasterDirect\n",
    "from skforecast.plot import set_dark_theme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c0024f6-5d9b-4e0d-a332-dfb71280d7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────────── <span style=\"font-weight: bold\">h2o</span> ───────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭────────────────────────────────────── \u001b[1mh2o\u001b[0m ───────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(\n",
    "    name=\"h2o\", raw=True, kwargs_read_csv={\"names\": [\"y\", \"datetime\"], \"header\": 0}\n",
    ")\n",
    "\n",
    "# Data preprocessing\n",
    "# ==============================================================================\n",
    "data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')\n",
    "data = data.set_index('datetime')\n",
    "data = data.asfreq('MS')\n",
    "data = data['y']\n",
    "data = data.sort_index()\n",
    "\n",
    "# Split train-test\n",
    "# ==============================================================================\n",
    "steps = 36\n",
    "data_train = data[:-steps]\n",
    "data_test  = data[-steps:]\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data_train.plot(ax=ax, label='train')\n",
    "data_test.plot(ax=ax, label='test')\n",
    "ax.legend();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9801a209-b601-4d9b-aa3c-a2e03df98c98",
   "metadata": {},
   "source": [
    "## Create and train forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4269b02b",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "<code>ForecasterDirect</code> includes the <code>n_jobs</code> parameter, allowing multi-process parallelization. This allows to train estimators for all steps simultaneously.\n",
    "\n",
    "The benefits of parallelization depend on several factors, including the estimator used, the number of fits to be performed, and the volume of data involved. When the <code>n_jobs</code> parameter is set to <code>'auto'</code>, the level of parallelization is automatically selected based on heuristic rules that aim to choose the best option for each scenario.\n",
    "\n",
    "For a more detailed look at parallelization, visit <a href=\"../faq/parallelization-skforecast.html\">Parallelization in skforecast</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "349d1b95-5b16-4620-a46e-d595b4187ebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    \n",
       "        <div class=\"container-6af5322cafcb40f3a5465750c14c9ada\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterDirect</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> Ridge</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_10']</li>\n",
       "                    <li><strong>Window size:</strong> 15</li>\n",
       "                    <li><strong>Maximum steps to predict:</strong> 36</li>\n",
       "                    <li><strong>Series name:</strong> y</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 14:56:46</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 14:56:46</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <MonthBegin></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.0001}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterdirect.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/direct-multi-step-forecasting.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "================ \n",
       "ForecasterDirect \n",
       "================ \n",
       "Estimator: Ridge \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15] \n",
       "Window features: ['roll_mean_10'] \n",
       "Window size: 15 \n",
       "Maximum steps to predict: 36 \n",
       "Series name: y \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <MonthBegin> \n",
       "Estimator parameters: \n",
       "    {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None,\n",
       "    'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.0001} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 14:56:46 \n",
       "Last fit date: 2025-11-26 14:56:46 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterDirect(\n",
    "                 estimator       = Ridge(),\n",
    "                 steps           = 36,\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=10)\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data_train)\n",
    "forecaster"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "733cef42-c7bf-42f8-8c3d-8471638599a4",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "When predicting, the value of `steps` must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1.\n",
    "\n",
    "+ If `int` only steps within the range of 1 to int are predicted.\n",
    "\n",
    "+ If `list` of `int`. Only the steps contained in the list are predicted.\n",
    "\n",
    "+ If `None` as many steps are predicted as were defined at initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f1bdf4f8-b999-4d97-899c-fbcc2af7066a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.952188\n",
       "2005-11-01    1.180630\n",
       "Name: pred, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "# Predict only a subset of steps\n",
    "predictions = forecaster.predict(steps=[1, 5])\n",
    "display(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0160892c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005-07-01    0.952188\n",
       "2005-08-01    1.004327\n",
       "2005-09-01    1.115190\n",
       "Freq: MS, Name: pred, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict all steps defined in the initialization.\n",
    "predictions = forecaster.predict()\n",
    "display(predictions.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1b53f4b-be13-4e7b-bfdd-2cf0f3492452",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data_train.plot(ax=ax, label='train')\n",
    "data_test.plot(ax=ax, label='test')\n",
    "predictions.plot(ax=ax, label='predictions')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92bce057-008e-462a-9c46-4557013783bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test error (mse): 0.008447518677148016\n"
     ]
    }
   ],
   "source": [
    "# Prediction error\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=36)\n",
    "error_mse = mean_squared_error(\n",
    "                y_true = data_test,\n",
    "                y_pred = predictions\n",
    "            )\n",
    "print(f\"Test error (mse): {error_mse}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dbe3c4dc-4b71-42f0-a4c9-d6815535272d",
   "metadata": {},
   "source": [
    "## Feature importances\n",
    "\n",
    "Since `ForecasterDirect` fits one model per step, it is necessary to specify from which model retrieves its feature importances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8394cca9-e86d-4309-b10c-ea26485f85e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>lag_12</td>\n",
       "      <td>0.551517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>lag_11</td>\n",
       "      <td>0.153757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lag_1</td>\n",
       "      <td>0.138408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>lag_13</td>\n",
       "      <td>0.056914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lag_2</td>\n",
       "      <td>0.050145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lag_3</td>\n",
       "      <td>0.043166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lag_10</td>\n",
       "      <td>0.019264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>roll_mean_10</td>\n",
       "      <td>0.016897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lag_9</td>\n",
       "      <td>0.010420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lag_8</td>\n",
       "      <td>-0.014137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lag_6</td>\n",
       "      <td>-0.014835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lag_5</td>\n",
       "      <td>-0.019494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lag_4</td>\n",
       "      <td>-0.021308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lag_7</td>\n",
       "      <td>-0.022662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lag_15</td>\n",
       "      <td>-0.035939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>lag_14</td>\n",
       "      <td>-0.071710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature  importance\n",
       "11        lag_12    0.551517\n",
       "10        lag_11    0.153757\n",
       "0          lag_1    0.138408\n",
       "12        lag_13    0.056914\n",
       "1          lag_2    0.050145\n",
       "2          lag_3    0.043166\n",
       "9         lag_10    0.019264\n",
       "15  roll_mean_10    0.016897\n",
       "8          lag_9    0.010420\n",
       "7          lag_8   -0.014137\n",
       "5          lag_6   -0.014835\n",
       "4          lag_5   -0.019494\n",
       "3          lag_4   -0.021308\n",
       "6          lag_7   -0.022662\n",
       "14        lag_15   -0.035939\n",
       "13        lag_14   -0.071710"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecaster.get_feature_importances(step=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d295d721",
   "metadata": {},
   "source": [
    "## Training and prediction matrices\n",
    "\n",
    "While the primary goal of building forecasting models is to predict future values, it is equally important to evaluate if the model is effectively learning from the training data. Analyzing predictions on the training data or exploring the prediction matrices is crucial for assessing model performance and understanding areas for optimization. This process can help identify issues like overfitting or underfitting, as well as provide deeper insights into the model’s decision-making process. Check the [How to Extract Training and Prediction Matrices](../user_guides/training-and-prediction-matrices.html) user guide for more information."
   ]
  }
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